{"id":"W104645259","doi":"10.1007/978-3-642-23094-3_11","title":"Interactive Segmentation with Super-Labels","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Pixel; Object (grammar); Histogram; Image segmentation; Coherence (philosophical gambling strategy); Computer vision; Image (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001648542,0.001609753,0.001733411,0.002131596,0.0009791408,0.002881132,0.002240539,0.002536194,0.01447904],"category_scores_gemma":[0.003952938,0.00183425,0.001592492,0.002260848,0.001095711,0.002583487,0.004533258,0.002354715,0.004071365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009244177,"about_ca_system_score_gemma":0.001158935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003292455,"about_ca_topic_score_gemma":0.008442852,"domain_scores_codex":[0.9986622,0.0002514647,0.0000721128,0.0003383823,0.0005020513,0.0001737132],"domain_scores_gemma":[0.9969627,0.001352157,0.0001583808,0.001027873,0.0003271983,0.00017163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002073575,0.0002128882,0.001377836,0.0008724165,0.0003177374,0.0007210215,0.0007520324,0.05475203,0.2117576,0.01727465,0.01933505,0.6905532],"study_design_scores_gemma":[0.0001178826,0.0001214219,0.001173252,0.0000976981,0.0001156564,0.000820522,0.0001646629,0.817718,0.1226029,0.0323237,0.02465381,0.00009055784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01050346,0.0003363833,0.9779367,0.0002066283,0.00008695202,0.00007205214,0.0002740142,0.007956208,0.002627664],"genre_scores_gemma":[0.09195314,0.0002544716,0.9002573,0.0002192392,0.0001050991,0.0001122135,0.0007472692,0.003202815,0.003148396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01447904,"threshold_uncertainty_score":0.04843724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040730788042411,"score_gpt":0.2787925118847826,"score_spread":0.2583852040043585,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}